Calibration of geometric parameters and error compensation of non-geometric parameters for cable-driven parallel robots

Zhang, Fei; Shang, Weiwei; Li, Guojiang; Cong, Shuang

doi:10.1016/j.mechatronics.2021.102595

Cited by 19 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Explicitly modelling this error in the chain is a challenging task as the error can be influenced by many external factors such as poses, weight of the attached tools, or mechanical compliance [22]. Hence, in addition to advantages of the proposed neural network-based method, the introduced objective function induces the change in the hand-eye parameters over time, instead of fitting the estimated transformation to the pre-determined hand-eye.…”

Section: B Discussionmentioning

confidence: 99%

Learning to Calibrate - Estimating the Hand-eye Transformation Without Calibration Objects

Pachtrachai

Vasconcelos

Edwards

et al. 2021

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Hand-eye calibration is a method to determine the transformation linking between the robot and camera coordinate systems. Conventional calibration algorithms use a calibration grid to determine camera poses, corresponding to the robot poses, both of which are used in the main calibration procedure. Although such methods yield good calibration accuracy and are suitable for offline applications, they are not applicable in a dynamic environment such as robotic-assisted minimally invasive surgery (RMIS) because changes in the setup can be disruptive and time-consuming to the workflow as it requires yet another calibration procedure. In this paper, we propose a neural network-based hand-eye calibration method that does not require camera poses from a calibration grid but only uses the motion from surgical instruments in a camera frame and their corresponding robot poses as input to recover the hand-eye matrix. The advantages of using neural network are that the method is not limited by a single rigid transformation alignment and can learn dynamic changes correlated with kinematics and tool motion/interactions. Its loss function is derived from the original hand-eye transformation, the re-projection error and also the pose error in comparison to the remote centre of motion. The proposed method is validated with data from da Vinci Si and the results indicate that the designed network architecture can extract the relevant information and estimate the hand-eye matrix. Unlike the conventional hand-eye approaches, it does not require camera pose estimations which significantly simplifies the hand-eye problem in RMIS context as updating the hand-eye relationship can be done with a trained network and sequence of images. This introduces a potential of creating a hand-eye calibration approach that is capable of accurately updating the hand-eye matrix according to the changes in RMIS setup. Index Terms-Calibration and Identification; Deep Learning Methods; Computer Vision for Medical Robotics I. INTRODUCTIONT He use of miniaturised surgical tools and camera to provide access to an operative site has transformed surgical practice such that the incision and trauma are minimised. When combining with robotic setups, the tele-manipulation

show abstract

Section: B Discussionmentioning

confidence: 99%

Learning to Calibrate - Estimating the Hand-eye Transformation Without Calibration Objects

Pachtrachai

Vasconcelos

Edwards

et al. 2021

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…[12]. They can be used as motion simulators [13], large-scale telescope orientation [14], large-scale camera systems [15], and construction [16], to name just a few examples even with applications to industrial scenarios as reported in [17][18][19]. CDPRs have been successfully implemented also in medical applications such as for assisting post-stroke or paraplegia patients such as reported for example in refs.…”

Section: Introductionmentioning

confidence: 99%

A novel pyramidal cable-driven robot for exercising and rehabilitation of writing tasks

Khadem,

Inel,

Carbone

et al. 2023

Robotica

View full text Add to dashboard Cite

This paper presents a novel cable-driven parallel robot with the aim of rehabilitating/exercising young and disabled children in drawing and writing tasks. The pyramidal topology was identified as providing the required three active translational Degrees of Freedom with a redundant set of five cables in a compact portable shape. In addition, this robot was designed with new features, as it is inexpensive, easy to control, easy to move to any place at home or school as it is fitting a home desk or classroom table. In this paper, we present the main steps for designing the proposed cable-driven pyramidal parallel robot. Specific attention is addressed to the design of the structure and the end effector as well as to establishing proper simulation models. Several simulations are proposed by implementing both kinematic and dynamic models to demonstrate the feasibility of multiple writing/drawing tasks. A specific user interface is proposed allowing both continuous and intermittent trajectories. A sliding mode control is implemented to achieve suitable tracking accuracy on a desired path. Finally, an experimental validation is successfully carried out by considering multiple trajectories for demonstrating the engineering feasibility and effectiveness of the proposed design and simulation models.

show abstract

“…Daney et al [8] used a method based on interval arithmetics for kinematic parallel robot calibration. In [11], a high precision and robustness iterative calibration method is proposed to significantly improve the endeffector position errors. For CDPRs, most of previous works relied on non-linear least square (NLLS) methods for parameter identification, as it applies conveniently to the minimization of the cable length residuals.…”

Section: Introductionmentioning

confidence: 99%

“…Proprioceptive motor position sensors are used in [12] for auto-calibration. Zhang et al [11] used only external measurement device to perform the iterative calibration method. Martin et al [13] used laser-based cable length measurement sensor to improve calibration quality.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Predicting the Calibration Accuracy in Planar Cable-Driven Parallel Robots

Wang

Cardou

Caro

2022

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

This paper presents a simulation of the calibration of a 3-DoF, 2-cable, planar cable-driven parallel robot (CDPR). The calibration is realized with the combination of a laser displacement sensor and an inclinometer attached to the moving platform. The actual accuracies of the sensors are tested at first for higher calibration quality. Through simulation, with more measurement poses used, the system variable identification errors are reduced, and have decreasing dispersion, finally form plateaus. The effect of each sensor on the calibration quality is studied. Based on the sensors considered in this work, the system variable errors are all within ±9 mm, and most are within ±5 mm for 5.209 m-span CDPR.

show abstract

Calibration of geometric parameters and error compensation of non-geometric parameters for cable-driven parallel robots

Cited by 19 publications

References 30 publications

Learning to Calibrate - Estimating the Hand-eye Transformation Without Calibration Objects

Learning to Calibrate - Estimating the Hand-eye Transformation Without Calibration Objects

A novel pyramidal cable-driven robot for exercising and rehabilitation of writing tasks

An Approach for Predicting the Calibration Accuracy in Planar Cable-Driven Parallel Robots

Contact Info

Product

Resources

About